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您的检索式：作者名="Brian M.Conley"
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| 1 | High-Content Screening and Analysis of Stem Cell-Derived Neural Interfaces Using a Combinatorial Nanotechnology and Machine Learning Approach显示文摘A systematic investigation of stem cell-derived neural interfaces can facilitate the discovery of the molecular mechanisms behind cell behavior in neurological disorders and accelerate the development of stem cell-based therapies.Nevertheless,high-throughput investigation of the cell-type-specific biophysical cues associated with stem cell-derived neural interfaces continues to be a significant obstacle to overcome.To this end,we developed a combinatorial nanoarray-based method for high-throughput investigation of neural interface micro-/nanostructures(physical cues comprising geometrical,topographical,and mechanical aspects)and the effects of these complex physical cues on stem cell fate decisions.Furthermore,by applying a machine learning(ML)-based analytical approach to a large number of stem cell-derived neural interfaces,we comprehensively mapped stem cell adhesion,differentiation,and proliferation,which allowed for the cell-type-specific design of biomaterials for neural interfacing,including both adult and human-induced pluripotent stem cells(hiPSCs)with varying genetic backgrounds.In short,we successfully demonstrated how an innovative combinatorial nanoarray and ML-based platform technology can aid with the rational design of stem cell-derived neural interfaces,potentially facilitating precision,and personalized tissue engineering applications. | Letao Yang Brian M.Conley Jinho Yoon Christopher Rathnam Thanapat Pongkulapa Brandon Conklin Yannan Hou Ki-Bum Lee | 2023 | Research2023，，1： | 0 |
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